Clustering Local L-1 Maxima

In the de novo mode analysis, after the local maxima have been identified from the tissue image, they are clustered.

The default clustering algorithm is based on Leiden community detection. SSAM also supports clustering using louvain, hdbscan and optics.

It can be initiated by:

analysis.cluster_vectors(method="leiden",
                         pca_dims=-1,
                         min_cluster_size=2,
                         max_correlation=1.0,
                         metric="correlation",
                         outlier_detection_method='medoid-correlation',
                         outlier_detection_kwargs={},
                         random_state=0,
                         **kwargs)

… where - method can be leiden, louvain, hdbscan, optics. - pca_dims are the number of principal componants used for clustering. - min_cluster_size is the minimum cluster size. - resolution is the resolution for Louvain community detection. - prune is the threshold for Jaccard index (weight of SNN network). If it is smaller than prune, it is set to zero. - snn_neighbors is the number of neighbors for SNN network. - max_correlation is the threshold for which clusters with higher correlation to this value will be merged. - metric is the metric for calculation of distance between vectors in gene expression space. - subclustering if set to True, each cluster will be clustered once again with DBSCAN algorithm to find more subclusters. - dbscan_eps is the eps value for DBSCAN subclustering. Not used when ‘subclustering’ is set False. - centroid_correction_threshold is the threshold for which centroid will be recalculated with the vectors which have the correlation to the cluster medoid equal or higher than this value. - random_state is the random seed or scikit-learn’s random state object to replicate the same result

Removing outliers

The cell type signature is determined as the centroid of the cluster. This can be affected by outliers, so SSAM supports a number of outlier removal methods:

analysis.remove_outliers(outlier_detection_method='medoid-correlation', outlier_detection_kwargs={}, normalize=True)

robust-covariance, one-class-svm, isolation-forest, local-outlier-factor - outlier_detection_kwargs are arguments passed to the outlier detection method